1. Field of the Disclosure
The present disclosure relates to image capture and device technologies that allow independent adjustment of the filtering for image regions by adapting to the image based on scene analysis.
2. Description of the Related Art
Traditionally, spectral imaging has relied on the use of a pre-determined set of filters that are mechanically or electronically adjusted to capture image bands with different spectral properties. Spectral imaging has been primarily confined to some niche high-end applications such as remote sensing and artwork analysis and archiving. Consumer level state-of-the-art spectral imaging systems are not readily available in the marketplace. This is due to several factors such as cost, complexity requiring fast computers, large data storage capacity, bulkiness of the imaging system and lack of a compelling application.
A typical spectral imaging system can be produced by combining an imaging detector with a filter wheel having color filters with different transmittances, or with liquid crystal tunable filters. A filter wheel has the disadvantage of being limited only to the existing filters in the filter wheel and the artifacts due to misregistration as a result of the mechanical movement of the filter wheel. On the other hand, electronic tuning using liquid crystal tunable filtering suffers from very low spectral transmittances that decrease the signal-to-noise ratio (SNR) of the imaging system. Thus, conventional spectral imaging systems have been hampered by a need to increase the number of captured signals to increase spectral resolution, reduce artifacts and increase SNR. Furthermore, spectral imaging systems are inherently by design very inefficient not just because of the tremendous redundancy in spectral information, but also because spectral imaging systems typically capture pre-determined channels regardless of whether or not there are meaningful information in the captured band. The capture of channels in areas of the images that there is no significant information in that spectral band leads to decrease in system SNR and consequently errors in spectral estimation.